PLAN DE PREPARACIÓN Y RESPUESTA PARA EMERGENCIAS En el presente documento se describe el Plan de Preparación y

Duration models are used to explain time to adoption of the six drugs as a function of the independent variables. When interpreting the results of a duration model, a negative marginal effect means a factor reduces duration, i.e. reduces the time to adoption (Baptista, 2000). Our preferred duration models, with marginal effects reported, are given in Table 4.4.39 Given that not all GPs have adopted each drug by the end of the sample period, the data is right-censored.40 The influence of rank, stock, order, learning-by-using and epidemic effects on time to adoption of the six new drugs are discussed below.

In the models we represent potential rank effects using a series of variables reflecting the characteristics of GPs and their practices. As presented in Table 4.4, GP practices with a nurse have lower times to adoption than those without a nurse for two of the drugs examined. This finding is statistically significant at the 1 per cent level for the antidepressant and statistically significant at the 5 per cent level for the hormonal contraceptive drug. However, it is worth noting that the size of the practice nurse effect is relatively small, in that time to adoption decreases by one to two weeks (0.25 and 0.56 of a month) for practices with a nurse in relation to these two drugs. Similarly, a decrease in time to adoption is reported for practices with a secretary in one of the six drugs, the antihistamine. This finding is statistically significant at the 5 per cent level. Again, this is a relatively small effect with time to adoption decreasing by approximately two weeks (-0.50 of a month) for practices with a

39 _{Initially, six baseline models were estimated, presented in Table A2.1. Subsequently, in a}

„stepwise‟ fashion, variables with z-statistics of less than |0.5| were excluded from the relevant failure time models. Comparison of Tables A2.1 and 4.4 suggests that the exclusion of a number of insignificant variables has little effect on coefficient signs and values.

40 _{As a robustness check, we removed the non-adopters for each drug from the sample and ran the}

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secretary in relation to one of these drugs. These findings are in line with much of the literature which report organisation size and human capital impacting positively on the adoption of innovations (Karshenas and Stoneman, 1993; Baptista, 2000). Also, as expected, GP age is found to have a statistically significantly positive effect on time to adoption in four of the drugs considered here (the antidepressant, the antihistamine, the smoking cessation drug and the hormonal contraceptive). However, the size of this effect is again relatively small, with time to adoption increasing in the region of 0.1 to 0.4 of a month for each increasing year. Following a systematic review of the literature, Masters (2008) reports similar findings in relation to doctors and internet adoption, with adoption being greater among younger doctors. There is also evidence of greater adoption of electronic patient records by younger GPs in Ireland (Meade et al., 2009).41

Results in relation to the dispensing practice variable are insignificant. Finally, the effect of IDTS, which reflects the effect on time to adoption of receipts from drug cost savings, impacts on one drug, the antihistamine. Time to adoption decreases by a little less than two weeks for the antihistamine drug for practices eligible for 50 per cent of savings from meeting prescribing targets and also for those practices that receive 60 per cent of savings relative to those practices eligible for 40 per cent of savings. It is worth acknowledging that the IDTS variables may also be capturing informational or experiential effects. The IDTS variables capture GPs that are willing to change their prescribing practices to meet budgetary targets, and are learning and gaining experience from these prescribing decisions.

41 _{However, not all small business adoption studies report significant findings in relation to age. For}

example, Burton et al (2003) report no statistically significant relationship between age of a farmer and the adoption of organic horticultural technology.

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Epidemic learning effects are captured in the models specifically by the rural practice variable. An increase in time to adoption is reported for practices in receipt of a rural practice allowance for two of the drugs examined. This finding is statistically significant at the 5 per cent level for the antidepressant and hormonal contraceptive drugs, with time to adoption increasing by 0.2 and 0.9 of a month for rural practices. Tamblyn et al. (2003) found similar results with lower utilisation rates of new drugs among physicians with a rural or remote practice location. Coleman et al. (1966) report in the classic drug diffusion study where physicians‟ decisions to prescribe a new antibiotic tetracycline were investigated that early adopters attend more out-of-town medical meetings that late adopters. While similar data is not available for this study, it is fair to suggest that GPs with practices in receipt of a rural practice allowance are less likely to be able to attend meetings and conferences than urban based GPs. Similarly, it is likely that such practices are visited less frequently by drug company representatives, which might be an important source of information for GPs in relation to prescribing decisions (Jones et al., 2001).

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Table 4.4: Duration models of time to first adoption – Preferred Models Esciatlopram1 mfx/se Esmoprazole2 mfx/se Rofecoxib3 mfx/se Desloratadine4 mfx/se Nicotine5 mfx/se Drospirenone & Estrogen6 mfx/se Rank Effects Practice Nurse -0.253*** -0.209 -0.08 -0.559** (0.062) (0.170) (0.164) (0.236) Practice Secretary -0.106 0.058 -0.504** -0.237* -0.581* (0.078) (0.095) (0.216) (0.131) (0.304) GP Age 0.012*** 0.003 0.020** 0.015** 0.044*** (0.004) (0.004) (0.010) (0.007) (0.014) Dispensing Practice 0.163 -0.42 -0.22 0.718* (0.108) (0.318) (0.188) (0.373) IDTS50 -0.044 0.013 -0.459** 0.082 -0.328 (0.071) (0.089) (0.196) (0.124) (0.276) IDTS60 -0.06 -0.135 -0.458*** -0.269 (0.063) (0.083) (0.176) (0.243) Epidemic Effects Rural Practice 0.181** 0.295 0.26 0.159 0.909** (0.083) (0.311) (0.225) (0.183) (0.380) Learning-By-Using Effects Portfolio Breadth -3.176*** -5.817*** -8.322*** -10.518*** -10.117*** -5.882*** (1.118) (1.861) (1.398) (3.181) (2.188) (1.292) Portfolio Breadth2 2.99 5.343 9.267 9.658** (2.360) (3.644) (6.597) (4.533) Order Effects -1.698*** -0.277* -6.016*** -8.635*** 0.694*** -1.044*** (0.128) (0.153) (0.374) (0.318) (0.130) (0.297) Stock Effects 12.375*** 10.531*** 15.954*** 6.155*** 20.632*** (0.208) (0.402) (0.949) (0.775) (0.935) N 23366 8607 8176 13628 10871 15082 Chi- squared 665.933 609.9 988.226 430.136 793.732 291.571 Log- likelihood 828.98395 294.391 395.43572 274.67431 714.38295 7.7404151 AIC -1627.97 -560.782 -768.871 -521.349 -1400.77 14.519 BIC -1507.08 -461.937 -691.773 -416.07 -1298.65 128.838

Notes: Models all include seasonal dummies (not reported). Models predict time after first adoption by any GP, hence N differs depending on time of first adoption. Specifically N denotes the number of GPs in each month who have not prescribed the drug. *** denotes significance at the 1 per cent level; ** at the 5 per cent level and * at the 10 per cent level. Variable definitions are given in Table 1. Drug Descriptions: 1 – Antidepressant; 2 - Proton Pump Inhibitor; 3 - Anti-inflammatory; 4 - Antihistamine; 5 - Smoking Cessation Medicines; 6 -Hormonal Contraceptive.

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The portfolio breadth variable, which was constructed to capture learning-by-using effects, reveals a consistent effect. Across all six drugs and statistically significant at the 1 per cent level, time to adoption decreases for GPs who prescribe drugs from larger portfolios. As demonstrated by the marginal effects for this variable, time to adoption decreases substantially for all six drugs for GPs who have larger prescribing portfolios. A percentage increase in a GPs portfolio decreases time to adoption of these drugs from three to ten and a half months. As discussed in the previous section, this relationship between portfolio size and time to adoption is non- linear in relation to the nicotine drugs, suggesting a u-shaped relationship between portfolio breadth and time to adoption. Previous studies have highlighted that being an early adopter of one drug does not impact on subsequent adoption decisions (Steffensen et al., 1999; Dybdahl et al., 2004; Kozyrskyj et al., 2007), however, our study highlights that the relative size of a GP‟s prescribing portfolio significantly impacts on the decision to prescribe a new drug. It is also possible that our portfolio breadth variable could also be considered a proxy for practice size, as the more patients a GP sees the more likely they require a larger portfolio of drugs from which to prescribe. Previous studies have reported higher utilisation rates of new drugs for larger practices, as measured by practice volume (Tamblyn et al., 2003).

In our duration models, potential order effects variables for each drug are represented by a dummy variable taking a value of one where a GP prescribed at least one of the remaining five drugs in the first six months after their first adoption. There are strongly statistically significant negative order effects for four of the six drugs. As is evident from Table 4.4, time to adoption decreases in these drugs for GPs who are deemed „first-movers‟ in the other drugs being examined. In relation to

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the antidepressant and hormonal contraceptive drugs, time to adoption decreases by approximately four to six weeks for „first-mover‟ GPs. The order effect is quite large in relation to the anti-inflammatory and antihistamine drugs with time to adoption decreasing by six and eight months for „first-movers‟ respectively. In standard terms, this suggests that some GPs are early adopters of new drugs in order to maximise the returns from that new adoption given anticipated future levels of adoption (Karshenas and Stoneman, 1993). It is important to note however that time to adoption of nicotine increases for „first mover‟ GPs. Previous literature has reported order effects in relation to the adoption of multiple technologies, where the technologies are complementary (Stoneman and Kwon, 1994) or simultaneously adopted (Stoneman and Toivanen, 1997). While the six drugs in this study are not complementary therapies, they were adopted over a similar time-period. A plausible explanation here is that, some GPs who for whatever reason were early adopters of one drug have learnt the benefits of early adopting, and therefore tend to be early adopters of other drugs.

Finally, in our models potential stock effects are represented by the proportion of GPs who had adopted a drug at any given point in time (Karshenas and Stoneman, 1993). We find a positive effect (i.e. time to adoption increases as the stock of previous adopters increases) for five of the six drugs examined. As is evident from Table 4.4, for each percentage increase in the stock of previous adopters, time to adoption increases from six to twenty months for these five drugs. It is unlikely GPs have sufficient information or ability to correctly anticipate future adoption patterns. However, the stock of previous adopters in all 6 drugs over the time-period in question is high relative to the non-adopters. For instance, within the first year of the

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adoption of all 6 drugs, over half of GPs in the sample have adopted them; in fact, sometimes this figure is greater than 70 per cent. Therefore, given the rapid adoption rates of these drugs, it is perhaps not surprising that as the stock of previous adopters increases time to adoption increases for the remaining GPs (some of whom may remain non-adopters). Stoneman and Kwon (1994) in a study of the adoption of complementary technologies also report evidence of stock effects.

In this chapter, we examine the factors which lie behind the rapid adoption by Irish GPs of six prescription drugs following their launch in the Irish market. Our study highlights a range of commonalities across all of the drugs considered and suggests the importance of GP and practice characteristics, strategic behaviour, informational and cumulative learning factors in shaping prescribing decisions. Our evidence on rank effects, intended to capture the differential benefit-cost ratio of adoption by GPs with different characteristics, largely mirrors that of other studies. Practices with either a nurse or clerical support are more likely to be early adopters of new drugs as are younger GPs. We also find evidence that the IDTS, designed primarily to reduce prescribing costs, may also be having additional benefits by stimulating early adoption. However, it is important to note that in general the size of these rank effects is relatively small in terms of reducing or increasing time to adoption.

More surprising, perhaps, is that we find strongly significant stock and order effects. GPs who have a track record of early adoption tend also to be early adopters of any new drug (order effect)42 and, the larger the proportion of GPs which have already

42 _{Being an early prescriber of one drug in our data does predict early adoption of some drugs.}

However it is not a strong predictor of being an early adopter of all drugs examined. For instance, no GP in the sample adopted all six drugs within the first six months of them being adopted. This contradicts the image of early adopters as being related to a general innovative predisposition.

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adopted a new drug the slower is subsequent adoption (stock effect). The standard interpretation of the stock and order effects in studies of new technology adoption by firms relates to the impact of the timing of adoption on the subsequent returns (Karshenas and Stoneman, 1993). Here, given the commercial autonomy of Irish GPs similar effects may be operating. Other potential, and observationally equivalent, explanations for these effects may relate more directly to information flows, a suggestion reinforced by the epidemic and learning-by-using effects we also find. Prescribing innovation tends to be slower in rural practices suggesting that isolated GPs with less opportunity for acquiring information about new drugs are slower to innovate. We find evidence of learning-by-using effects influencing the timing of first prescription of all six drugs examined, with slower adoption among GPs with narrower prescribing portfolios.

It is worth noting that the availability of similar drugs in the same therapeutic class may affect the uptake of each of the drugs examined. While Irish GPs‟ prescribing decisions are not constrained by NICE guidelines or based on the British Medical Formulary (BMF), it would be important to consider how availability of and information concerning alternative treatments may influence adoption decision- making. In Chapter 8, we discuss future research possibilities, one of which is an analysis of the determinants of adoption of drugs from the same therapeutic class. The availability of similar drugs could be incorporated in the study by including the „stock‟ of adopters of alternative drugs in the analysis.

Therefore, it appears that a GPs decision to prescribe is heavily dependent on the new drugs in question (Dybdahl et al. 2005, Steffensen et al, 1999).

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The duration models used enable the consideration of a wide range of factors on the timing of prescribing innovations. In fact, the innovation literature highlights the lack of panel data in relation to adoption of new innovations (Battisti et al., 2007). Three important limitations of this empirical study are worth highlighting, however. First, a common idea in the literature is that new drugs diffuse into general practice through a two-step model with hospital consultants as the innovators and GPs as the followers. In other words, it is the consultant who initially prescribes the new drug and GPs repeat prescribe these drugs when the patient returns to the primary care setting. Florentinus‟ (2006) study of the adoption of new drugs in a Danish primary care setting, however, contradicts this two-step model. While acknowledging the influence of medical specialists in GPs‟ prescribing decisions, Florentinus (2006) finds that GPs themselves are responsible for a considerable amount of all early prescriptions for new drugs. Here, data restrictions mean that we are not able to control for the potential influence of hospital consultants on GPs‟ prescribing decisions. However, of the six drugs examined, four (the antihistamine, the smoking cessation medication, the hormonal contraceptive and the antidepressant) are unlikely to be repeat prescriptions following an initial prescription by a hospital consultant. It is perhaps more likely that prescribing decisions for the proton pump inhibitor and the anti-inflammatory considered here may be more strongly influenced by hospital consultants‟ initial prescribing decisions. Secondly, due to data restrictions we do not control for the impact of advertising in relation to GPs decisions to adopt these six drugs. However, this is something to be considered in future research. Advertising noise or impact could be measured through a citation search in medical journals or ranking the market power of the pharmaceutical companies which initially released these drugs.

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As discussed in the methodology section, an advantage of duration models is the ability to control for unobserved heterogeneity. However, our duration models did not fit when we attempted to control for unobserved heterogeneity.43 Jenkins (2003) acknowledges that the frailty models can be relatively „fragile‟ in the statistical sense, as they can be relatively hard to fit particularly if the frailty variance is close to zero. Jenkins (2003) highlights three sources of potential bias in „non-frailty‟ duration models. Firstly there is potential to over-estimate the degree of negative duration dependence, and under-estimate the degree of positive duration dependence. Secondly, the proportionate effect of a given regressor on the hazard rate may no longer be constant and independent of survival time. Thirdly, the estimate of a positive (negative) coefficientderived from the non-frailty model will underestimate (over-estimate) the „true‟ estimate. Jenkins (2003) also reports that the empirical literature generally confirms these theoretical propositions. However, he concludes that if a fully flexible specification of the baseline hazard function is used, then the magnitude of the biases in the „non-frailty‟ model is diminished. While unobserved heterogeneity is likely to exist in these duration models, our explanatory variables differentiate between individual GPs well.

4.5 Conclusion

The purpose of this chapter is to explain the determinants of timing of first prescription of new drugs by GPs. The Irish primary health care system provides a distinctive setting for such an examination. The commercial and prescribing autonomy which characterise Irish general practice suggests that prescribing

43 _{The Stata command „streg‟ is used in our analysis. We included the „frailty‟ option to control for}

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decisions may reflect both commercial and medical factors. Six drugs new to the Irish market were identified for inclusion in the analysis, all of which were prescribed by Irish GPs to GMS patients in an extremely short time period from authorisation under the GMS scheme (see Table 4.1 and Figure 4.1a-e). Using data pertaining to 625 Irish GPs, duration analysis is performed to determine the equilibrium, disequilibrium and learning-by-using determinants of prescribing innovation (See Table 4.5 for a symbolic summary of results).

Our study finds some evidence of rank effects in relation to adoption of new prescription drugs by GPs. Practices with nursing and clerical support tend to be early adopters of new drugs, and younger GPs tend to be early adopters of new drugs. We find strongly significant and consistently signed, learning-by-using, stock and order effects across these drugs; GPs with broader prescribing portfolios tend to be early adopters of new drugs, GPs that have a track record of early adoption tend to be early adopters of other new drugs, and the larger the proportion of GPs which have already adopted a new drug the slower is subsequent adoption. Epidemic effects are also evident with slower adoption by rural practices.

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Table 4.5: Symbolic Summary of Anticipated Results and Results